MB-SwinRefiner: Mammographic Mass Segmentation with Probability-Guided Residual Refinement

Accurate mammographic mass segmentation is crucial for computer-aided breast cancer diagnosis, but remains challenging because masses may be small, low-contrast, irregularly shaped, and partially obscured by dense fibroglandular tissue. Although recent methods have improved contextual representation and multi-scale feature extraction, reliable segmentation still requires better integration of lesion-scale representation, contour-derived auxiliary supervision, and local probability-map refinement. This paper proposes MB-SwinRefiner, a novel two-stage framework consisting of a Multi-scale Boundary-aware SwinUNet (MB-SwinUNet) stage and a probability-guided residual refinement stage. The first stage, MB-SwinUNet, generates an initial mass probability map using hierarchical Swin encoding, multi-scale decoder fusion, and training-only auxiliary boundary supervision. The second stage refines this prediction through probability-guided residual correction by using the Stage 1 probability map as a prior and learning logit-space corrections. Experiments were conducted on a case-level five-fold cross-validation of the INbreast dataset under both a resized benchmark setting and a native sliding-window full-mammogram setting. Performance was evaluated using Dice, intersection over union, sensitivity, and specificity. In the resized benchmark setting, MB-SwinRefiner achieved Dice of 86.19%, IoU of 84.33%, sensitivity of 89.34%, and specificity of 99.98%. In the native sliding-window setting, MB-SwinRefiner improved mean Dice from 76.64% to 78.06% and mean IoU from 74.38% to 76.11%. These results suggest that combining Swin-based contextual encoding, multi-scale decoder fusion, contour-derived training supervision, and probability-guided residual refinement can improve region-based mammographic mass segmentation performance.